Lab 06

First, I need to read in the data:

housedata <- read_csv("House_all_members.csv")

Now I need to create a year column:

housedata %>%
  mutate(
    year = 1789 + 2 * (congress - 1)
  ) -> housedata

Confirm that the years have the appropriate range:

range(housedata$year)

## [1] 1789 2017

Make a “polished” plot of the nominate_dim1 scores over time:

ggplot(housedata) +
  aes(x = year,
      y = nominate_dim1) +
  geom_point(alpha = 0.2,
             color = "gray") +
  labs(x = NULL,
       y = "DW-scores",
       title = "Ideology of Congress over Time")

Do the same by party code and use geom_gitter:

ggplot(housedata) +
  aes(x = year,
      y = nominate_dim1,
      color = factor(party_code)) +
  geom_jitter(alpha = 0.1,
              show.legend = F) +
  labs(x = NULL,
       y = "DW-scores",
       title = "Ideology of Congress over Time")

Just focus on polarization after Reconstruction (post 1876):

house1876 <- housedata %>%
  filter(year > 1876)

Make a twoparty dummy:

`%nin%` <- Negate(`%in%`)
house1876 %>%
  mutate(
    twoparty = ifelse(
      party_code %nin% c(100, 200),
      NA,
      ifelse(party_code==200, 1, 0)
    )
  ) -> house1876

Now add rep_or_dem:

house1876 %>%
  mutate(
    rep_or_dem = ifelse(twoparty==0,
                        "Democrat",
                        "Republican")
  ) -> house1876

Reps should be positive and Dems negative:

house1876 %>%
  group_by(rep_or_dem) %>%
  summarize(mean(nominate_dim1, na.rm=T))

## # A tibble: 3 × 2
##   rep_or_dem `mean(nominate_dim1, na.rm = T)`
##   <chr>                                 <dbl>
## 1 Democrat                            -0.317 
## 2 Republican                           0.368 
## 3 <NA>                                -0.0835

Now let’s make another plot with colors by the two parties:

ggplot(house1876) +
  aes(x = year,
      y = nominate_dim1,
      color = rep_or_dem) +
  geom_jitter(alpha = 0.1) +
  labs(x = NULL,
       y = "DW-score",
       title = "Ideology in Congress over Time",
       color = NULL) +
  ggpal(type = "binary",
        na.translate = F)

Alright, one more thing. Adjust the x-axis to be more granular and at an angle:

ggplot(house1876) +
  aes(x = year,
      y = nominate_dim1,
      color = rep_or_dem) +
  geom_jitter(alpha = 0.1) +
  labs(x = NULL,
       y = "DW-score",
       title = "Ideology in Congress over Time",
       color = NULL) +
  ggpal(type = "binary",
        na.translate = F) +
  scale_x_continuous(
    breaks = seq(1877, 2017, 10)
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Now let’s test some claims about polarization over time. I want to show the average for each party and the total average over time to compare:

house1876 %>%
  group_by(congress, rep_or_dem) %>%
  mutate(party_mean = mean(nominate_dim1, na.rm=T)) %>%
  group_by(congress) %>%
  mutate(
    congress_mean = mean(nominate_dim1, na.rm=T)
  ) -> house1876

house1876 %>%
  drop_na(rep_or_dem) %>%
  ggplot() +
  aes(x = year) +
  geom_line(aes(y = party_mean,
                     color = rep_or_dem)) +
  geom_line(aes(y = congress_mean),
                 color = "black") +
  ggpal(type = "binary") +
  scale_x_continuous(
    breaks = seq(1877, 2017, by = 10) 
  ) +
  labs(x = NULL,
       y = "Mean DW-Scores",
       title = "Mean DW-Scores over Time by Party",
       color = NULL,
       caption = "Note: Black line is the total mean") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Let’s do some statistical tests. Is one party more polarized than the other?

house1876 %>%
  ungroup %>%
  select(year, congress_mean, party_mean, rep_or_dem)%>%
  distinct()%>%
  drop_na() %>%
  pivot_wider(values_from = party_mean,
              names_from = rep_or_dem) -> house1876cong

Now let’s recreate the last figure but in a kinda-sorta hacky way:

set_palette(
  qualitative = c("black", "blue", "red"),
  binary = c("blue", "red"),
  from_coolors = F
)
ggplot(house1876cong) +
  aes(x = year) +
  geom_line(
    aes(y = congress_mean, 
        color = "All Members")
  ) +
  geom_line(
    aes(y = Republican,
        color = "Republicans")
  ) +
  geom_line(
    aes(y = Democrat,
        color = "Democrats")
  ) +
  ggpal() +
  labs(x = NULL,
       y = "Mean DW-score",
       title = "Mean DW-scores over Time by Party",
       color = NULL)

Is one party more polarized than the other?

lm(abs(Democrat) - abs(Republican) ~ 1, house1876cong) %>%
  summary()

## 
## Call:
## lm(formula = abs(Democrat) - abs(Republican) ~ 1, data = house1876cong)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.079752 -0.041540  0.001586  0.032937  0.098765 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.038942   0.005717  -6.812 2.75e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.04817 on 70 degrees of freedom

It looks like the Reps are farther to the right than the Dems are to the left. But the difference is actually quite small.